Abstract
Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the world have taken a keen interest in this issue and finally introduced the concept of the smart grid. Smart grid is an ultimate solution to all of the energy related problems of today’s modern world. In this paper, we have proposed a meta-heuristic optimization technique called the dragonfly algorithm (DA). The proposed algorithm is to a real-world problem of single and multiple smart homes. In our system model, two classes of appliances are considered; Shiftable appliances and Non-shiftable appliances. Shiftable appliances play a significant role in demand side load management because they can be scheduled according to real time pricing (RTP) signal from utility, while non-shiftable appliances are not much important in load management, as these appliances are fixed and cannot be scheduled according to RTP. On behalf of our simulation results, it can be concluded that our proposed algorithm DA has achieved minimum electricity cost with a tolerable waiting time. There is a trade-off between electricity cost and waiting time because, with a decrease in electricity cost, waiting time increases and vice versa. This trade-off is also obtained by our proposed algorithm DA. The stability of the grid is also maintained by our proposed algorithm DA because stability of the grid depends on peak-to-average ratio (PAR), while PAR is reduced by DA in comparison with an unscheduled case.
Highlights
In today’s developing era, everything is changing very quickly
The real time pricing (RTP) signal is provided to the users via smart energy meters, the users modify their daily needs according to the RTP signal, and the EMC scheduled the smart appliances according to the proposed algorithm
Simulation results show that the meta-heuristic optimization technique dragonfly algorithm (DA) reduces the cost by 35.57% and 17.22%, while Genetic Algorithms (GAs) reduces the electricity cost by 30.54% and 06.56% in single and multiple home scenarios, respectively
Summary
In today’s developing era, everything is changing very quickly. In all aspects of life, things are changing and improving to provide maximum comfort to the end users. In an incentive-based technique, the user’s appliance is switched to an ON/OFF state by sending a short message to the smart home (SH); when a high peak is detected, the appliance is switched to off state and vice versa In this way, the PAR is decreased by the company, which is the main objective in an optimization problem. According to [5], electricity consumption can be decreased 10–30% by scheduling of appliances intelligently It shows that scheduling can perform well in the objectives of an optimization problem. Researchers try to capture the natural phenomenon in their algorithms, by capturing several nature-inspired, natural phenomena, bio-inspired and meta heuristic inspired algorithms were developed They have explored and analyzed some other algorithms for problems related to energy optimization like: Bio-inspired Genetic Algorithms (GAs) and Cuckoo Search Algorithm (CSAs) [7]. The simulation results are compared with a well-known optimization algorithm, GA
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